Study on intestinal parasitic infections and gut microbiota in cancer patients at a tertiary teaching hospital in Malaysia

Intestinal parasitic infections (IPIs) can lead to significant morbidity and mortality in cancer patients. While they are unlikely to cause severe disease and are self-limiting in healthy individuals, cancer patients are especially susceptible to opportunistic parasitic infections. The gut microbiota plays a crucial role in various aspects of health, including immune regulation and metabolic processes. Parasites occupy the same environment as bacteria in the gut. Recent research suggests intestinal parasites can disrupt the normal balance of the gut microbiota. However, there is limited understanding of this co-infection dynamic among cancer patients in Malaysia. A study was conducted to determine the prevalence and relationship between intestinal parasites and gut microbiota composition in cancer patients. Stool samples from 134 cancer patients undergoing active treatment or newly diagnosed were collected and examined for the presence of intestinal parasites and gut microbiota composition. The study also involved 17 healthy individuals for comparison and control. Sequencing with 16S RNA at the V3–V4 region was used to determine the gut microbial composition between infected and non-infected cancer patients and healthy control subjects. The overall prevalence of IPIs among cancer patients was found to be 32.8%. Microsporidia spp. Accounted for the highest percentage at 20.1%, followed by Entamoeba spp. (3.7%), Cryptosporidium spp. (3.0%), Cyclospora spp. (2.2%), and Ascaris lumbricoides (0.8%). None of the health control subjects tested positive for intestinal parasites. The sequencing data analysis revealed that the gut microbiota diversity and composition were significantly different in cancer patients than in healthy controls (p < 0.001). A significant dissimilarity was observed in the bacterial composition between parasite-infected and non-infected patients based on Bray–Curtis (p = 0.041) and Jaccard (p = 0.021) measurements. Bacteria from the genus Enterococcus were enriched in the parasite-infected groups, while Faecalibacterium prausnitzii reduced compared to non-infected and control groups. Further analysis between different IPIs and non-infected individuals demonstrated a noteworthy variation in Entamoeba-infected (unweighted UniFrac: p = 0.008), Cryptosporidium-infected (Bray–Curtis: p = 0.034) and microsporidia-infected (unweighted: p = 0.026; weighted: p = 0.019; Jaccard: p = 0.031) samples. No significant dissimilarity was observed between Cyclospora-infected groups and non-infected groups. Specifically, patients infected with Cryptosporidium and Entamoeba showed increased obligate anaerobic bacteria. Clostridiales were enriched with Entamoeba infections, whereas those from Coriobacteriales decreased. Bacteroidales and Clostridium were found in higher abundance in the gut microbiota with Cryptosporidium infection, while Bacillales decreased. Additionally, bacteria from the genus Enterococcus were enriched in microsporidia-infected patients. In contrast, bacteria from the Clostridiales order, Faecalibacterium, Parabacteroides, Collinsella, Ruminococcus, and Sporosarcina decreased compared to the non-infected groups. These findings underscore the importance of understanding and managing the interactions between intestinal parasites and gut microbiota for improved outcomes in cancer patients.


Microbial profile of cancer patients in Malaysia
In this study, out of 43 samples that tested positive for parasites, 33 parasite-infected cancer patients and 20 non-infected cancer patients, along with 17 healthy controls, were included in the microbiota analysis.The remaining 10 positive samples were excluded due to difficulties in DNA extraction from small stool samples.Five samples were excluded after sequence processing and sample filtering to ensure an even number of reads per sample.Additionally, due to differences in characteristics between cancer patient groups and the influence of host phenotypic variables on gut microbiota composition, all symptomatic patients were excluded to investigate the effects of IPIs on cancer microbial profiles at an asymptomatic level.

Association between intestinal parasite infection and gut microbiota
The PCoA plot of all distance matrices shows parasite-infected and non-infected clusters separate from the control sample (Fig. 2).Moreover, based on Bray-Curtis and Jaccard index distance matrices, a significant dissimilarity was observed in the microbial composition between parasite-infected and non-infected patients.The PERMANOVA supported these findings with 999 permutations (Table 3).Further analysis between different IPIs and non-infected individuals demonstrates a noteworthy variation in Entamoeba-infected and Cryptosporidium-infected samples, as indicated by unweighted UniFrac (pseudo-F = 1.66; p = 0.008) and Bray-Curtis (pseudo-F = 1.42; p = 0.034) distance matrices, respectively.The structure of microbial communities between microsporidia-infected and non-infected individuals also appears to be dissimilar, as observed in three distance matrices (unweighted; pseudo-F = 1.56, p = 0.026; weighted: pseudo-F = 2.43, p = 0.019; Jaccard: pseudo-F = 1.35, p = 0.031).No significant dissimilarity was observed between Cyclospora-infected groups and non-infected groups (Table 3).
Taxonomic analysis at the species level comparing parasite-infected and non-infected patients with control shows disparity in the prevalence of bacterial species (Fig. 3).Specifically, the relative abundance of unclassified bacteria species within the genus Enterococcus was significantly higher in the infected group (8.5%) compared to both the non-infected (0.196%) and control groups (0.001%), with a significant difference of p < 0.001.Conversely, Faecalibacterium prausnitzii demonstrated a significantly lower relative abundance in the parasite-infected group (0.576%) when compared to the non-infected (8.65%; p < 0.001) and control group (5.12%; p = 0.03).No significant difference was observed when comparing the relative abundance of F. prausnitzii between non-infected patients and the control (p > 0.05).Additionally, the relative abundance of Prevotella copri was  significantly reduced in both infected (0.0036%; p < 0.001) and non-infected (0.051%; p = 0.01) patients when compared to the control (8.28%).
To identify taxonomic differences associated with intestinal parasites, we used the LEfSe algorithm to compare different types of intestinal parasites significantly different from non-infected samples, as observed in the beta analysis.Based on LEfSe analysis, bacteria from the Enterococcus genus and Bacillales order were enriched in microsporidia-infected samples, with concurrent decreases in bacteria from the Clostridiales order, Faecalibacterium, Parabacteroides, Collinsella, Ruminococcus, and Sporosarcina.Meanwhile, only minor changes were observed in the gut microbiota based on beta diversity in individuals infected with Entamoeba spp.and Cryptosporidium spp.Bacteria from Clostridiales were enriched with Entamoeba infections, whereas those from Coriobacteriales decreased.Bacteroidales and Clostridium were found in higher abundance in the gut microbiota with Cryptosporidium infection, while Bacillales decreased (Fig. 4).

Discussion
Recent research has shown changes in gut microbiota diversity and composition in response to intestinal parasites [12][13][14] .However, most studies have been conducted in animal models, with only a few in humans 12,13 .These changes can have positive or negative effects on overall health, depending on the types of parasites present in the gut.Cancer patients are often susceptible to opportunistic infections, including intestinal parasites.Several studies have shown gut microbiota dysbiosis in cancer patients 16 .However, little is known about the influence of intestinal parasites on gut microbiota among cancer patients in Malaysia.Therefore, this study aims to understand gut microbiota composition among cancer patients with intestinal parasites.Our study revealed significant alpha and beta diversity differences between non-infected cancer patients and healthy controls.These findings support the hypothesis that gut microbiota diversity is reduced in a disease state, and the microbial composition differs from that of healthy individuals 17 .Parasitic infections had little effect on the alpha microbial diversity compared to non-infected patients.However, there was a significant dissimilarity in the gut microbiota composition between parasite-infected and non-infected patients, possibly driven by Cryptosporidium spp., Entamoeba spp., and microsporidia infections.
The results of this study demonstrated that intestinal parasitic infections (IPIs) can influence gut microbiota, as confirmed by previous studies [12][13][14] .However, the relationship between intestinal microbiota diversity may depend on which parasite is present in the gut 18 .Notably, studies have also shown that Giardia spp. or Entamoeba spp.alone can alter microbial communities 12,14 .For example, von Huth et al. 12 reported that helminth infections largely did not affect microbial diversity, while protozoan infections such as Entamoeba spp.and Giardia spp.moderately affected the alpha diversity.In a study among asymptomatic children in Argentina, gut microbiota diversity decreased with increased Giardia burden or co-infection with Giardia-helminth but increased with helminth infection alone 13 .Meanwhile, in Toro-Londono et al. 's 14 study, Cryptosporidium spp.showed no significant alterations to the bacterial microbiota regarding diversity and structure among children in Colombia.Cryptosporidium and Entamoeba both multiply in the intestinal enterocytes.
Consequently, the impact of these infections on the gut microbiota community could be indirect, as the parasites adhere to and lyse the colonic epithelium.Various studies have shown an increased abundance of bacteria that produce metabolites, representing the microbial response to mucosal or epithelial damage caused by these infections 14 .In this study, we observed a significant enrichment of the taxa Clostridium in Cryptosporidium infections.Similarly, the Clostridiales order increased in Entamoeba infections but decreased in abundance with microsporidia infections.Clostridium spp. is the primary commensal bacterial cluster in the gut that synthesises   important metabolites such as butyrate, indole propionic acids, and secondary bile acids, which play a crucial role in maintaining intestinal homeostasis 19 .It has been noted that they reduce allergic reactions and inflammation.
Previous studies have reported contradictory findings regarding the abundance of Clostridium in helminths 20,21 and protozoan infections 12,21 .As previously reported, von Huth et al. 12 observed an increase in the abundance of bacteria from two Clostridium clades (Clostridium IV & VIVb) and a decrease in one Clostridium clade (Clostridium XVIII) based on the types of protozoan infections.In northern India, the abundance of Clostridiales decreases in E. histolytica-infected patients due to acute or chronic diarrhoea 21 .In mixed infections with T. trichiura and A. lumbricoides, the abundance of bacteria from the genus Clostridia class decreases among children 20 .However, Easton et al. 22 reported a significant increase in the proportion of Clostridiales following albendazole treatment and helminth clearance.These parasites are likely to influence gut physiology indirectly or directly; hence, the number of Clostridium spp.has decreased, as seen in cases of intestinal failure and ulcerative colitis.
Additionally, bacteria from the Bacteroidales order were also enriched with Clostridium in Cryptosporidium infections, while the Bacillales decreased.Briefly, bacteria from the Bacteroidales order include Bacteroides, Prevotella, and Parabacteroides genera.Bacteroides are the predominant genus from the phyla Bacteroidetes in the gut microbiota of humans 23 .They can express polysaccharide A, which can stimulate the development of regulatory T cells and the production of cytokines that protect against colitis 24 .These findings may imply that Cryptosporidium spp.provides beneficial effects to asymptomatic cancer patients, consistent with previous findings 14,25 .Similarly, bacteria such as Coriobacteriaceae and Lactobacillus increased in abundance in the gut of asymptomatic mice carriers with Cryptosporidium spp. 25 .
A study using an animal model found that antibiotics and diarrhoea may worsen the severity of the disease [26][27][28] .For example, Lactobacillus bacteria increased in untreated mice infected with Cryptosporidium but declined in mice pre-treated with antibiotics such as cloxacillin 27 .Additionally, a study on goats with mild to severe clinical symptoms of cryptosporidiosis, including growth retardation, diarrhoea, hypothermia, and mortality, showed a depletion of bacteria relevant to the synthesis of SCFA 28 .In a study by Carey et al. 26 among Bangladeshi children, low abundances of Megasphaera were associated with diarrheal symptoms before and during Cryptosporidium infections compared to subclinical samples 26 .
Two other studies have found a similar relationship between the abundance of Coriobacteriaceae and Entamoeba infections.Yanagawa et al. 29 reported high levels of Coriobacteriaceae, Ruminooccaceae, and Clostridiaeae, and a low abundance of Streptococaceae in asymptomatic E. histolytica infections.On the other hand, von Huth et al. 12 observed a decrease in Collinsella and Clostridium XVIII, while Clostridium IV increased in individuals infected with E. histolytica.These reported differences in findings could be due to geographical variations in gut microbiota.However, both studies confirm that Entamoeba infections, similar to Cryptosporidium infections, increase the number of beneficial bacteria in their hosts.Furthermore, Verma et al. 21observed a significant reduction in the abundance of metabolite-producing bacteria such as Bacteroides, C. coccoides subgroup, C. leptum subgroup, Lactobacillus, Campylobacter, and Eubacteroim and an increase in Bifidobacterium while no change in Ruminococcus in patients with amoebic dysentery, which worsened the disease severity.
On the other hand, this study also attempts to determine the gut microbiota composition with microsporidia infections.To our knowledge, no prior research has looked at the impact of microsporidian infections on the human gut microbiota.At the same time, reports about the correlations mostly use animal models [30][31][32] .In this study, we observed the enrichment of opportunistic pathogens such as Enterococcus and Bacillales in microsporidia infections with a concurrent decrease in abundance of beneficial bacteria such as Collinsella, Parabacteroides, Clostridiales (i.e.Faecalibacterium, Ruminococcus), and Sporosarcina.Based on our findings, microsporidia infections negatively correlate with patient health.Unlike in Cryptosporidium and Entamoeba infections, this study observed an increased risk for opportunistic infections by Enterococcus and Bacillales in asymptomatic patients with microsporidian infections while metabolites-producing bacteria depletes.
Based on a previous study, microsporidia infections positively correlated with lactic acid bacteria from the genus Weissella 31 .According to Trzebny, except for acid-tolerant species, microsporidians may reduce the gut pH and inhibit most bacteria growth.Our results concur with these observations based on the enrichment of the two taxa (i.e., Enterococcus and Bacillales).Enterococcus spp.and Bacillales (particularly Staphylococcus) bacteria can survive in an acidic environment 33,34 .Furthermore, microsporidia infections in silkworms have similarly reported enrichment of Enterococcus spp.such as E. faecalis LX10 32 .Interestingly, Zhang reported that E. faecalis benefits silkworms by reducing microsporidia spp.spore germination rate and infection efficiency.Furthermore, E. faecalis produces lactic acid, which reduces the gut pH, thus inhibiting silkworm germination and lowering gut injury in silkworms.Therefore, the current research observed that the types of effects seen by intestinal protozoan could be different.While previous studies show Giardia and Entamoeba are the only protozoan species capable of changing the gut microbiota alone, this study provides evidence that protozoa such as Cryptosporidium and microsporidia are capable of affecting the gut microbiota of patients with cancer.
Furthermore, this study shows that both Cryptosporidium spp.and Entamoeba spp.could have beneficial effects on the human host while asymptomatic but can be negatively correlated when accompanied by antibiotics and diarrhoea.Additionally, this study has observed that microsporidia spp.promotes enrichment of possibly opportunistic pathogenic bacteria with a reduction in bacteria that maintains the gut barrier integrity.While Zhang et al. 32 observed the beneficial effects of Enterococcus spp. in ameliorating microsporidian infections in silkworm microbiota, a negative correlation to the health of patients with cancer, as previously reported 35 , must also be considered.Therefore, the role of Enterococcus in patient health and microsporidian infection cannot be fully elucidated in this study and, thus, requires further investigations.
Like many other tropical countries, Malaysia faces challenges related to parasitic infections.This research highlights the importance of including the detection of intestinal parasites in routine diagnostic tests, considering the persistent prevalence of these infections among cancer patients in Malaysia.By exploring the relationship between intestinal parasitic infections (IPIs) and gut microbiota in Malaysian cancer patients, valuable insights can be gained into the dynamic changes caused by these infections and their impact on the gut microbial community.Analysing the altered taxa in this study contributes to a better understanding of the metabolites synthesised in response to these infections.Despite the documented dysbiosis of the gut microbiota in cancer patients, as evidenced in various literature, this research demonstrates that the intestines still play a substantial role in influencing the gut microbiota composition.
In this study, we acknowledge several limitations.Firstly, due to the strict procedures and movement control orders imposed during the COVID-19 lockdown, only single stool samples were collected from patients for convenience just a few months after sample collection began.Moreover, many patients were reluctant to participate in prolonged research due to ongoing treatment and emotional distress related to their health conditions, potentially underestimating the actual prevalence of IPIs 36 .Future work should consider examining three consecutive stool samples from similar patients.This study could also not recruit sample match pairs for the healthy control due to the COVID-19 restrictions.Thus, the datasets from healthy subjects used in this study were obtained from a previously published article by our group 37 .Healthy control included were of Malaysian locality to minimize environmental variance that may influence the gut microbiota.This study also did not collect data on cancer types, stages, chemotherapy cycles, and treatment duration due to ethical approval limitations restricted access to the patient medical records.We hope future studies will consider these factors based on our baseline findings, as reported here.
Additionally, the study did not collect information on patient diets, an important factor in understanding dysbiosis in cancer patients 38 , which should be considered in future research.Furthermore, as this study is not longitudinal, rapid microbiota changes in cancer patients due to the influence of intestinal parasites could not be captured.A longitudinal study would require patient follow-up, potentially leading to a lack of cooperation.Moreover, since participants were recruited from a single hospital, potential regional variations in gut microbiota could not be assessed.Therefore, multi-centre sample collection would be necessary to validate the present findings further.Another limitation was the small number of participants per group.Previous research has shown inconsistent results in gut microbiota composition in different types of cancer, potentially due to certain cancer types weakening the patient immune system or other treatment protocols.However, to ensure sample homogenisation, the findings could not be extrapolated based on cancer types to determine the effects of IPIs on different cancer types.
Last but not least, the detection of parasites relied on microscopy examination.We recognise that in cases where microscopy is not adequate due to the morphological similarities of the parasites, advanced molecular and immunological techniques are often required.However, the choice of method depends on factors such as availability, cost, and the specific requirements of the diagnostic setting.While our study only utilised microscopy, it remains a gold-standard diagnostic method for parasitology.For example, the concentration technique is known for its high sensitivity.Additionally, we employed the permanent staining procedure, a common practice in diagnostic laboratories for intestinal parasite infections.Nonetheless, it is important to consider other advanced screening techniques to obtain a more accurate result, which should be explored in future studies.
In conclusion, this study provides a preliminary understanding of the impact of intestinal parasites on the gut microbiota of cancer patients in a Malaysian hospital.Despite the limited samples, the study observed significant differences in gut microbiota composition in patients infected with Cryptosporidium spp., Entamoeba spp., and microsporidia compared to healthy controls.This study observed that microsporidia spp.may promote the enrichment of potentially opportunistic pathogenic bacteria from the genus Enterococcus in cancer patients.Conversely, while Cryptosporidium spp.and Entamoeba spp., are associated with an enrichment in metaboliteproducing bacteria, microsporidia infections appear to diminish these bacteria.This nuanced understanding may be important for future research and developing therapies such as probiotics, which are tailored to the needs of cancer patients.Moreover, this study underscores the importance of acknowledging intestinal parasites as significant contributors to alterations in the microbiota in future research.Further research should aim to understand better the relationship between gut microbiota and intestinal parasites in cancer patients using a more comprehensive dataset.

Ethical approval
This study was approved by the Medical Ethics Committee, University of Malaya Medical Centre (UMMC), [MREC ID NO: 2019528-7454].Before the study commencement, participants received an oral briefing from the investigator regarding the objectives and methodology of the study.All procedures adhered to relevant guidelines and regulations sanctioned by the Ethics Committee.Participants were assured that the methods employed carried no inherent risks and that their identities would remain confidential.It was explicitly stated that participation was voluntary, allowing them to withdraw without explanation.For participants aged 16 and above, informed consent was acquired through written signatures or verbal confirmation, followed by a thumbprint for those who were illiterate.In cases of participants under 16 years, informed consent was obtained from a parent or legal guardian through signed documentation or a thumbprint.

Sample design
Over 12 months, a convenient sampling method was utilised to gather 134 stool samples from cancer patients aged one and above at the Oncology Unit, UMMC.The inclusion criteria required participants to be diagnosed with cancer, either newly diagnosed or receiving active treatment, to have refrained from taking antibiotics within a month before sample collection, and to have written consent.Demographic information such as age, gender, personal identification, diagnosis, and date of cancer therapy were obtained from their hospital records.Patients not meeting the selection criteria were excluded.To investigate the connection between IPIs and gut microbiota, the gut microbiota of stool samples that tested positive for parasites were compared with negative stool samples, using healthy controls as a baseline.The study included 33 microscopically positive stool samples and 20 negative samples.A sample was considered positive if at least one parasite was observed microscopically.Due to the COVID-19 restriction, sample match pairs of healthy controls could not be recruited.Thus, the datasets from healthy subjects were obtained from a previously published article by our team 37 .The study inclusion criteria for the control group (i.e., baseline) include healthy individuals from Malaysia with no history of parasitic diseases or cancer.

Sample collection, microscopic examination, and DNA extraction
Single stool samples were collected in a screw-capped container and divided into fresh and fixed portions in 2.5% potassium dichromate (1:1 dilution).The freshly obtained samples were immediately stored at − 80 °C for microbiome analysis, while the preserved samples were kept at 4 °C for microscopic examination.Due to the COVID-19 lockdown, movement was restricted, and laboratory access was limited.This led to the prompt preservation of all collected stool samples in potassium dichromate and refrigeration before examination, rendering them unsuitable for culture techniques.Consequently, we selected the most appropriate method based on the study objectives and the available resources given the prevailing circumstances.As a result, more sensitive techniques, particularly for hookworm and Strongyloides, such as the Harada Mori culture, could not be performed during the study period as it required a fresh sample and daily monitoring.
The stool samples underwent processing using direct smear and formalin-ether concentration technique for ova, trophozoites, or cysts.Permanent staining methods such as modified Ziehl-Neelsen and Gram-chromotrope Kinyoun (GCK) staining were then used to detect intestinal protozoa.Following the manufacturer's protocol, DNA was extracted from fresh stool samples using the FavorPrep™ Stool DNA Isolation Mini kit (Favorgen®, Taiwan).The concentration and quality of the extracted DNA were measured using NanoDrop Spectrophotometers (NanoDrop Technologies, USA).Finally, the extracted DNA was stored at − 20 °C until further use.
A PCR reaction was performed in triplicate in a 25 μL reaction mixture containing 12.5 μL of 2× KAPA HiFi HotStart Ready Mix, 5 μL of each forward and reverse primer (1 μM), and 5 ng of template DNA PCR amplifications consisting of 3 min of initial denaturation at 95 °C, followed by 25 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, extension at 72 °C for 30 s and final elongation at 72 °C for 5 min.According to the manufacturer's instructions, PCR products were purified with AMPure XP beads (Beckman Coulter, U.S.).The extracted DNA quantity and quality were analysed by fluorometer with dsDNA binding dyes using Agilent DNA 1000 Kit (Agilent, Germany).Sample libraries were constructed and pooled in equimolar and paired-end sequences (2 × 250 bp) on an Illumina Miseq platform.

16S rRNA gene sequences processing
The raw FASTQ.files containing paired-end sequences were imported into the Quantitative Insights into Microbial Ecology 2 (QIIME2) software package version 2021.4 40 for demultiplexing, trimming, and filtering low-quality bases.Initially, the raw sequence data were demultiplexed to remove the barcode sequence.The demultiplexed Illumina paired ends were assembled, and paired-end reads overlapping by more than 10 bp were trimmed for Illumina adapters and primers.Readings that could not be assembled and contained 2 nucleotide mismatches in primer matching were discarded.The trimmed paired-end reads were joined using a q2-vsearch plugin 41 and filtered based on the quality score.Joined reads were truncated at any site, receiving an average quality score of < 20, and the truncated reads shorter than 50 bp containing ambiguous characters were removed.
The joined reads were denoised using Deblur (q2-deblur plugin) 42 to filter out noisy sequences, remove chimeric sequences, remove singletons, and dereplicate the sequences to produce feature data and a table known as Amplicon Sequence Variant (ASV).The ASV feature data were rarefied to 5,191 reads per sample for further downstream analyses (q2-feature table rarefy plugin) 43 .Rarefied ASVs were aligned by mafft alignment, and FastTree was applied to generate a phylogenetic tree (q2-phylogeny plugin) 44 .

16S rRNA gene sequence diversity analysis
Samples were assessed for alpha diversity (variation in community composition between samples) and beta diversity (microbial diversity within samples) using metrics available in q2-diversity plugins.A Kruskal-Wallis pair-wise statistic was employed for cross-sectional analysis to assess the significance of alpha diversity and differences between groups.A p-value of less than 0.05 was considered significant 45 .Meanwhile, beta diversity was measured using weighted and unweighted UniFrac distances.Additional tests were conducted using Bray-Curtis (quantitative) and Jaccard (qualitative) distances to quantify microbial dissimilarity between samples.Subsequently, principal coordinate analysis (PCoA) 46 was conducted on the distance matrices to identify environments that could influence the grouping of similar communities.The resulting PCoA plots were visualised using the "ggplot2" package in R. To test for significance in group distances, PERMANOVA tests with 999 permutations were utilised 47 .
Furthermore, the ASVs were taxonomically classified using a q2-feature classifier against the pre-trained Greengenes 13_8 core database 99% OTUs reference sequences, trimmed for the 16S rRNA V3-V4 regions.Taxonomic analyses were performed at the phyla and genus levels.The statistical significance of differentially abundant phyla and genera between groups was assessed using the Kruskal-Wallis test.The resulting p-values were corrected for false discovery rates using the p.adjust function with the Benjamin-Hochberg method implemented in R. All results were visualised using the "ggplot2" package in R.

Identification of confounding variables
Several host phenotypic and environmental variables, such as age, gender and diet, have been linked to differences in gut microbiota composition.In this study, no information on the patient diet was obtained.Furthermore, given the differences in the characteristics between patient cohorts, preliminary diversity analyses were performed to identify the confounding variable and minimise bias in our analysis.In this study, significant differences were observed between symptomatic and asymptomatic samples.Thus, symptomatic samples were excluded from this study.No significant differences in microbial richness and composition were observed in other pairs of groups regardless of gender, race, cancer group and the status of cancer treatment.

LDA effect size (LefSe) analysis
The LefSe analysis, conducted using the Galaxy online interface (http:// hutte nhower.sph.harva rd.edu/ galaxy/), aimed to identify the key bacterial taxa showing differential abundance between microbiome pairs.The comparison classes were based on the types of parasite infections, comparing cohorts of infected and non-infected patients.Initially, LefSe identified statistically different features among the biological classes.Subsequently, a non-parametric factorial Kruskal-Wallis (KW) rank-sum test was performed, and a linear discriminant analysis model was used to estimate the effect sizes of the identified features, determining whether they are consistent with the expected behaviour of the different biological classes 48 .

Statistical analysis
Data were analysed using Excel (Microsoft Corporation, US) and SPSS software (Statistical Package for the Social Sciences, version 25.0, SPSS Inc Chicago, III, USA).Demographic data, including age, gender, personal identification, diagnosis, and date of cancer therapy, were treated as categorical variables.Categorical variables were presented as frequency (per cent) and 95% confidence intervals (95% CI).When appropriate, a Chi-square or Fisher's exact test was conducted to identify any differences among the variables.A p-value of less than 0.05 was considered significant.

Figure 1 .
Figure 1.Bacteria phyla compositions among patients with cancer.Age-related stacked bar plots were used to display the distribution of the most abundant phyla in each patient sample.These top 6 phyla (Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Verrucomicrobia and Fusobacteria) made up almost 99% of the abundance, with the remaining taxa being categorised as 'other' .

Figure 3 .
Figure 3.Comparison of microbial composition in parasite-infected, non-infected and healthy control at species level.Age-related stacked bar plots were used to display the distribution of the 20 most abundant phyla in the patient sample group by parasite infection, with the remaining taxa being categorised as 'other' .

Figure 4 .
Figure 4. Different abundances of microbial communities between (a) Microsporidium spp., (b) Entamoeba spp.and (c) Cryptosporidium spp.compared to non-infected groups.A cladogram displays the relationship between the significantly distinct taxa at different tiers with the clade as a group of organisms that shares a common ancestor.*Significant difference LDA score ≥ 4.0, p ≤ 0.05.

Table 1 .
Overall prevalence of intestinal parasitic infections (IPIs) based on microscopy (N = 134).N total number of samples, n number of positive samples, 95% CI confidence interval.

Table 2 .
Demographic characteristics of the study population.N total number of samples, IQR Interquartile range, SD standard deviation, ALL acute lymphoblastic leukaemia, AML acute myeloid leukaemia, JMML juvenile myelomonocytic leukaemia, LCH Langerhans cell histiocytosis.